In cases where multiple statistical comparisons are performed, it is common practice to apply a correction to the raw \(p\) -values. The Bonferroni correction [1]} is calculated by dividing the desired level of significance \((\alpha = 0.05)\)  by the number of comparisons. The Benjamini–Yekutieli procedure [2]} controls the false discovery rate under arbitrary dependence assumptions, and is less conservative than the Bonferroni correction. Given \(m\)  null hypotheses and their corresponding \(p\) -values \(P_1,...,P_m\) , the \(p\) -values are sorted in ascending order. For a given level of significance, in our case \(\alpha = 0.05\) , reject the null hypothesis for the first \(k\)  values that satisfy \(P_k \le k\alpha / (m * c(m))\)  where \(c(m) = \sum _{i=1}^m 1/i\) .
